Metabolite_APOE_assoc
Zeyuan Song & Paola Sebastiani
January 2023
Initialize paths
knitr::opts_chunk$set(echo = TRUE)
setwd("/restricted/projectnb/llfs/LinGA_protected/analysis/genomics/metabolomics/paola_analysis/age_change/")
dir() ## [1] "2023.01.18.llfs.metab.age.change.csv"
## [2] "2023.03.31.llfs.metab.age.change.csv"
## [3] "2024.06.14.llfs.metab.age.change.csv"
## [4] "2024.06.22.llfs.metab.age.change.csv"
## [5] "Age_rel.change.assoc_batch4.pc_gee.06.14.2024.csv"
## [6] "Age_rel.change.assoc_batch4.pc_gee.06.22.2024.csv"
## [7] "Age_rel.change.assoc_batch4.pc_genesis.03.31.2023.csv"
## [8] "Age_rel.change.assoc_batch5.pc_gee.06.14.2024.csv"
## [9] "Age_rel.change.assoc_batch5.pc_gee.06.22.2024.csv"
## [10] "Age_rel.change.assoc_batch5.pc_genesis.03.31.2023.csv"
## [11] "Metabolite.batch4_age_relative.change_assoc_03.31.2023.Rmd"
## [12] "Metabolite.batch4_age_relative.change_assoc_03.31.2023.html"
## [13] "Metabolite.batch4_age_relative.change_gee.06.14.2024.Rmd"
## [14] "Metabolite.batch4_age_relative.change_gee.06.14.2024.html"
## [15] "Metabolite.batch5_age_relative.change_assoc_03.31.2023.Rmd"
## [16] "Metabolite.batch5_age_relative.change_assoc_03.31.2023.html"
## [17] "Metabolite.batch5_age_relative.change_gee.06.14.2024.Rmd"
## [18] "Metabolite.batch5_age_relative.change_gee.06.14.2024.html"
## [19] "aggregate_res.html"
## [20] "aggregate_res.rmd"
## [21] "analysis.final.no.missing.dat.batch4.csv"
## [22] "analysis.final.no.missing.dat.batch5.csv"
## [23] "annotated_Age_rel.change_assoc_batch4.pc_gee.06.14.2024.csv"
## [24] "annotated_Age_rel.change_assoc_batch4.pc_gee.06.22.2024.csv"
## [25] "annotated_Age_rel.change_assoc_batch4.pc_genesis.03.31.2023.csv"
## [26] "annotated_Age_rel.change_assoc_batch5.pc_gee.06.14.2024.csv"
## [27] "annotated_Age_rel.change_assoc_batch5.pc_gee.06.22.2024.csv"
## [28] "annotated_Age_rel.change_assoc_batch5.pc_genesis.03.31.2023.csv"
## [29] "archived"
## [30] "plot_dir"
## [31] "results"
## [32] "run.all.o47338"
## [33] "run.all.o47360"
## [34] "run.all.o47616"
## [35] "run.all.o47619"
## [36] "run.all.o6731500"
## [37] "run.all.o6731805"
## [38] "run.all.o6731810"
## [39] "run.all.o6745611"
## [40] "run.all.o8069748"
## [41] "run.all.o8071790"
## [42] "run.all.o8072902"
## [43] "run.all.o8073016"
## [44] "run.all.o8073512"
## [45] "run.all.o8074280"
## [46] "run.all.o8074309"
## [47] "run.all.o8084021"
## [48] "run.all.o8084022"
## [49] "run.all.o8084112"
## [50] "run.all.o8084129"
## [51] "run.all.o8084254"
## [52] "run.all.o8084454"
## [53] "run.all.o8214887"
## [54] "run.all.o8214986"
## [55] "run.all.o8214997"
## [56] "run.all.o8215010"
## [57] "run.all.o8215103"
## [58] "run.all.o8215105"
## [59] "run.all.o8216530"
## [60] "run.all.o8216740"
## [61] "run.all.o8222441"
## [62] "run.batch4.2.qsub"
## [63] "run.batch4.qsub"
## [64] "run.batch5.2.qsub"
## [65] "run.batch5.qsub"
## [66] "run_aggregate.sub"llfs.pheno.dir <- "/restricted/projectnb/llfs/LinGA_protected/analysis/genomics/metabolomics/paola_analysis/generate_list_metabolomics/"
llfs.metab4.dir <- "/restricted/projectnb/llfs/LLFS_omics/LLFS_metabolomics/batch4_20220506/"
llfs.metab3.dir <- "/restricted/projectnb/llfs/LLFS_omics/LLFS_metabolomics/batch3_20220325/"
llfs.metab5.dir <- "/restricted/projectnb/llfs/LLFS_omics/LLFS_metabolomics/batch5_20221220/"
annot.dir <- "/restricted/projectnb/necs/Data_library/Metabolomics/NECS/metabolic_data/annotation/"
library(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(Heatplus)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --## v tibble 3.1.7 v purrr 0.3.4
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(coxme)## Loading required package: survival
## Loading required package: bdsmatrix
##
## Attaching package: 'bdsmatrix'
##
## The following object is masked from 'package:base':
##
## backsolvelibrary(GENESIS)
suppressPackageStartupMessages(library(SeqArray))
suppressPackageStartupMessages(library(SeqVarTools))
library(Biobase)## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
##
## The following objects are masked from 'package:dplyr':
##
## combine, intersect, setdiff, union
##
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
##
## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min
##
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.library(lubridate)##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:BiocGenerics':
##
## intersect, setdiff, union
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(geepack)Read data and basic QC
Read LLFS list forbatch 4
use fake names of metabolites for analysis and annotate at the end
use data in the long format and analyze using GEE
llfs.data.batch5 <- read.csv(paste0(llfs.pheno.dir, "llfs.data.metabolom.batch5.change.03.31.2023.csv"),
header=T, na.strings = c("", NA), check.names=F) %>%
# generate age at blood.2
mutate(Age.blood.2 = year(dmy(date.blood))-BirthYear) %>%
mutate( delta.t = Age.blood.2-Age.e)
dim(llfs.data.batch5)## [1] 3898 20 met.5.llfs <- readr::read_csv(paste0(llfs.metab5.dir, "peak_areas_pos_neg_merged_imputed_normalized.20221220.csv")) %>%
mutate( fake.subject = paste(subject, visitcode, sep="_"), .after = visitcode) ## Rows: 3937 Columns: 222
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (222): subject, visitcode, DL-2-Aminooctanoic acid, Homostachydrine, 2-A...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message. n.metab <- ncol(met.5.llfs)-3
orig.metab.names <- names(met.5.llfs)[4:ncol(met.5.llfs)]
fake.metab.names <- paste0("metab", c(1:n.metab))
metab.look.up.table <- data.frame( orig.metab.names, fake.metab.names)
names(met.5.llfs)[4:ncol(met.5.llfs)] <- fake.metab.namesData formatting–
met.llfs <- met.5.llfs
dim(met.llfs)## [1] 3937 223 table(met.llfs$visitcode)##
## 1 3 4
## 2686 1246 5# extract patients data at visiti 1 or enrolled at visit 2
met.llfs.vst1 <- met.llfs %>%
filter(visitcode==1 | visitcode==4)
met.data.1 <- as.data.frame(t(met.llfs.vst1[ , 4:ncol(met.llfs.vst1)]))
dim(met.data.1)## [1] 220 2691colnames(met.data.1) <- met.llfs.vst1$subject
# extract patients data at visit2
met.llfs.vst2 <- met.llfs %>%
filter(visitcode==3)
met.data.2 <- as.data.frame(t(met.llfs.vst2[ , 4:ncol(met.llfs.vst2)]))
dim(met.data.2)## [1] 220 1246colnames(met.data.2) <- met.llfs.vst2$subject
common.id <- intersect(met.llfs.vst1$subject, met.llfs.vst2$subject)
common.metab <- intersect(row.names(met.data.1), row.names(met.data.2))
length(common.id)## [1] 1207 length(common.metab)## [1] 220 ## Generate data with visit 1 and 2 metabolomic data
met.data.1 <- as.data.frame(met.data.1[ match(common.metab, row.names(met.data.1)), match(as.character(common.id), names(met.data.1) )])
names(met.data.1) <- paste0(names(met.data.1), "_1")
met.data.2 <- as.data.frame(met.data.2[ match(common.metab, row.names(met.data.2)), match(as.character(common.id), names(met.data.2) )])
names(met.data.2) <- paste0(names(met.data.2), "_3")
met.data <- cbind(met.data.1, met.data.2)Data visualization and outlier detection in met.data.1
met.data.1 <- met.data
hist(apply(met.data.1,2,min))
summary(apply(met.data.1,2,min))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.66 2695.45 4902.19 5249.32 7160.51 46605.56boxplot((met.data.1))
pca.res <- prcomp(log(t(met.data.1)), scale. = T)
# summary(pca.res)
plot(pca.res$x[,1:2])
outliers <- names(which(pca.res$x[,1] < -20))
ok.samples <- setdiff(names(met.data.1), outliers)
new.met.data.1 <- met.data.1[ , as.character(ok.samples)]
pca.res <- prcomp(log(t(new.met.data.1)), scale. = T)
## summary(pca.res)
plot(pca.res$x[,1:2])
# filter out bad samples
met.data.1 <- data.frame(fake.subject = ok.samples, t(met.data.1[ , ok.samples]))
dim(met.data.1)## [1] 2413 221 # now drop outliers
n.outlier <- c()
for(ind.col in 2:ncol(met.data.1)){
this.metab <- log(met.data.1[,ind.col])
this.mean <- mean(this.metab, na.rm=T)
this.var <- var(this.metab, na.rm=T)
set.to.na <- which((this.metab > this.mean+4*sqrt(this.var)) |
(this.metab < this.mean-4*sqrt(this.var)))
met.data.1[set.to.na, ind.col ] <- exp(this.mean)
n.outlier <- c(n.outlier, length(set.to.na))
}
summary(n.outlier)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 4.000 7.018 8.000 88.000 hist(n.outlier)
# Order by ID
met.data.1 <- met.data.1[order(met.data.1$fake.subject),]generate analysis data set
analysis.master.dat <- met.data.1 %>%
left_join( llfs.data.batch5, by = c("fake.subject"))
dim(analysis.master.dat)## [1] 2413 240 ## correct delta age for long format analysis
analysis.master.dat$delta.t[analysis.master.dat$visitcode == 1] <- 0pca and grm
pc.df <- read.csv("/rprojectnb2/llfs/LinGA_protected/analysis/genetics/GWAS_EL_Zeyuan/LLFS_PCA_GRM/llfs_pcair.csv")
analysis.dat <- left_join(analysis.master.dat, pc.df, by=c("subject"="sample.id"))
#png("PC1vsPC2_4577.png")
ggplot(analysis.dat, aes(PC1, PC2)) + geom_point()## Warning: Removed 114 rows containing missing values or values outside the scale range
## (`geom_point()`).
#dev.off()
#png("PC3vsPC4_4577.png")
ggplot(analysis.dat, aes(PC3, PC4)) + geom_point()## Warning: Removed 114 rows containing missing values or values outside the scale range
## (`geom_point()`).
#dev.off()
#PCA plots look good
#80 with missing PCsassociation
library(gee)
analysis.final.dat <- analysis.dat %>%
mutate(FC_DK = FC == "DK") %>%
select( row.names(met.data), fake.subject, subject, visitcode,
Age.e, delta.t, Sex, Education, FC_DK, PC1, PC2, PC3, PC4,
htn_meds, lipid_meds, nitro_meds, t2d_meds)
dim(analysis.final.dat)## [1] 2413 236#1717
var.list <- c("Age.e", "delta.t",
"Education","Sex","FC_DK","PC1","PC2","PC3","PC4","htn_meds","lipid_meds","nitro_meds","t2d_meds")
summary(analysis.final.dat[,var.list])## Age.e delta.t Education Sex
## Min. : 36.0 Min. : 0.000 Min. : 2.00 Length:2413
## 1st Qu.: 56.0 1st Qu.: 0.000 1st Qu.:10.00 Class :character
## Median : 63.0 Median : 5.000 Median :14.00 Mode :character
## Mean : 65.8 Mean : 4.236 Mean :12.69
## 3rd Qu.: 73.0 3rd Qu.: 8.000 3rd Qu.:15.00
## Max. :102.0 Max. :12.000 Max. :17.00
## NA's :4
## FC_DK PC1 PC2 PC3
## Mode :logical Min. :-0.02359 Min. :-0.08599 Min. :-0.12233
## FALSE:2104 1st Qu.:-0.01226 1st Qu.:-0.01010 1st Qu.:-0.00196
## TRUE :309 Median :-0.00796 Median :-0.00323 Median : 0.00289
## Mean : 0.00169 Mean :-0.00436 Mean :-0.00061
## 3rd Qu.: 0.00903 3rd Qu.: 0.00683 3rd Qu.: 0.00809
## Max. : 0.07540 Max. : 0.07442 Max. : 0.04129
## NA's :114 NA's :114 NA's :114
## PC4 htn_meds lipid_meds nitro_meds
## Min. :-0.05021 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:-0.00702 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 0.00431 Median :0.0000 Median :0.0000 Median :0.0000
## Mean : 0.00413 Mean :0.4557 Mean :0.3715 Mean :0.2358
## 3rd Qu.: 0.01324 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. : 0.09302 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :114 NA's :157 NA's :157 NA's :157
## t2d_meds
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.06206
## 3rd Qu.:0.00000
## Max. :1.00000
## NA's :157analysis.final.no.missing.dat <- na.omit(analysis.final.dat)
dim(analysis.final.no.missing.dat)## [1] 2142 236#1635
analysis.final.no.missing.dat[,row.names(met.data)] <- log(analysis.final.no.missing.dat[,row.names(met.data)])
write.csv(analysis.final.no.missing.dat, "analysis.final.no.missing.dat.batch5.csv")
out_dat <- c(); j <-0
for(i in row.names(met.data)){
j <- j+1
analysis.final.no.missing.dat$outcome <- analysis.final.no.missing.dat[,i]
mod <- gee(outcome ~ delta.t+Age.e+Sex+Education+FC_DK+PC1+PC2+PC3+PC4+
htn_meds+lipid_meds+nitro_meds+t2d_meds,
id = subject,
corstr = "exchangeable", data=analysis.final.no.missing.dat)
coeff <- as.data.frame(summary(mod)$coeff)
coeff$pval <- 2*(1-pnorm(abs(coeff[, "Robust z"])))
out_dat <- rbind(out_dat, data.frame(metabolite = row.names(met.data)[j],
time_eff = coeff["delta.t","Estimate"],
time_sd = coeff["delta.t","Robust S.E."],
time_pval = coeff["delta.t","pval"],
Age_eff = coeff["Age.e","Estimate"],
Age_sd = coeff["Age.e","Robust S.E."],
Age_pval = coeff["Age.e","pval"],
Male_eff = coeff["SexMale","Estimate"],
Male_sd = coeff["SexMale","Robust S.E."],
Male_pval = coeff["SexMale","pval"],
Educ_eff = coeff["Education","Estimate"],
Educ_sd = coeff["Education","Robust S.E."],
Educ_pval = coeff["Education","pval"],
FC.DK_eff = coeff["FC_DKTRUE","Estimate"],
FC.DK_sd = coeff["FC_DKTRUE","Robust S.E."],
FC.DK_pval = coeff["FC_DKTRUE","pval"],
PC1_pval = coeff["PC1","pval"],
PC2_pval = coeff["PC2","pval"],
PC3_pval = coeff["PC3","pval"],
PC4_pval = coeff["PC4","pval"],
htn_med_eff = coeff["htn_meds","Estimate"],
htn_med_pval = coeff["htn_meds","pval"],
lipid_med_eff = coeff["lipid_meds","Estimate"],
lipid_med_pval = coeff["lipid_meds","pval"],
nitro_med_eff = coeff["nitro_meds","Estimate"],
nitro_med_pval = coeff["nitro_meds","pval"],
t2d_med_eff = coeff["t2d_meds","Estimate"],
t2d_med_pval = coeff["t2d_meds","pval"]))
}## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.733119087 0.009013721 0.003457100 -0.096737519 -0.008022164 0.172248455
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.914591456 0.776506021 0.200496374 -1.233367527 0.050233445 0.124572132
## nitro_meds t2d_meds
## 0.090918506 0.386914641## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.268273e+01 -2.468559e-02 8.604959e-03 -3.651780e-02 3.817791e-06
## FC_DKTRUE PC1 PC2 PC3 PC4
## -1.418635e-01 -1.422456e+00 2.087595e+00 2.662189e-02 -3.166560e+00
## htn_meds lipid_meds nitro_meds t2d_meds
## 1.218771e-02 9.563366e-02 8.430542e-02 4.197985e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.069557161 0.008007251 0.007698846 0.071425507 -0.001941652 0.129717795
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.717146344 -0.340645578 -0.495349222 -0.490995691 0.021776528 0.053245183
## nitro_meds t2d_meds
## 0.064060307 -0.056254565## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.685832288 0.006469890 0.006874077 0.215943281 -0.003863832 -0.079589707
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.158419480 0.031819359 0.072119761 -0.180106748 0.007886937 0.021779916
## nitro_meds t2d_meds
## 0.066528758 0.027561649## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.2338048761 -0.0115929021 0.0054115939 0.0456037858 -0.0004170792
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0753911400 0.7101397382 0.5082425066 -1.5468120454 1.1933244836
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0452009575 -0.0114742320 0.0517068224 -0.1716907842## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.235125e+01 -2.414402e-03 6.849701e-05 -2.555362e-03 8.463992e-04
## FC_DKTRUE PC1 PC2 PC3 PC4
## 1.597916e-01 -8.691045e-03 -3.415860e-02 -1.467433e-01 4.784854e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## 7.420510e-03 -1.465237e-03 -1.607655e-02 -5.554370e-03## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.2413202191 -0.0006496512 -0.0005612039 0.0007840833 -0.0002933028
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0191890962 -0.1996178661 0.1537697622 -0.0855796611 -0.6699556426
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0065598667 0.0127246058 -0.0005198097 -0.0019404695## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.390982277 -0.014414199 0.018222178 0.206658104 -0.007349897 1.785154032
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 5.154849115 -5.711616896 -1.989710145 2.802558657 -0.023892840 -0.054724893
## nitro_meds t2d_meds
## -0.059254435 0.272226773## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.825810e+01 2.674230e-03 2.653620e-03 1.749028e-01 -4.786881e-05
## FC_DKTRUE PC1 PC2 PC3 PC4
## 7.410987e-02 4.760851e-01 -3.515536e-01 1.211644e-01 4.374623e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## 1.710292e-02 4.629250e-03 5.852433e-03 -2.510697e-03## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.674045042 0.002135185 0.011809643 -0.003991984 0.005188619 -0.022000040
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 4.692165131 2.185388534 0.810163890 1.471815412 0.118536455 -0.002177212
## nitro_meds t2d_meds
## 0.060548954 -0.035548535## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.128225800 0.007210991 0.003678823 -0.009873539 0.004101242 -0.003007953
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.295496491 0.268540311 0.249514334 -0.291820592 -0.022562364 -0.008856147
## nitro_meds t2d_meds
## 0.025973089 0.062556311## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.8051217252 -0.0022991818 0.0010836217 0.0215661912 -0.0002179875
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0324153252 0.2992615427 -0.0609614081 -0.3743490702 -0.0233407751
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0189863614 0.0064893855 0.0116503528 -0.0277490330## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.199788216 -0.001628424 -0.000226143 -0.006082547 -0.003688864 0.003913156
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.889665023 -0.227469534 0.028408813 0.924471802 -0.002127794 0.008343256
## nitro_meds t2d_meds
## -0.001943520 0.011562384## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.109666180 0.004117775 -0.007944410 0.025677982 0.012823704 0.735060808
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.313595532 -1.562396645 -4.622862254 2.450790646 0.190689467 0.025877057
## nitro_meds t2d_meds
## 0.024720317 -0.062024517## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.3804009885 0.0119727601 0.0054814247 0.0295251513 -0.0003188995
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1076127068 -0.3127424824 -0.2652046606 0.0615582921 -1.4267375369
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0625397929 0.0036903823 0.0538119162 0.0464438097## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.2252801915 -0.0042116450 0.0017255024 -0.0011592694 -0.0020937889
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.1078424684 -0.0337321237 0.0516067051 -0.2765531046 0.4332142967
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0077957490 0.0004718581 -0.0090428110 -0.0109312803## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.350707243 0.005818445 0.013090424 0.040859322 0.007977222 -0.353667378
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.113721852 0.975313314 0.358998519 -1.612997216 0.053623060 0.094250990
## nitro_meds t2d_meds
## 0.053566473 0.030117909## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.74683146 0.01618432 0.01573028 0.04613133 -0.01162460 -0.20702367
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.55893396 -1.15095697 -1.57078118 1.21007475 0.17738914 0.11455227
## nitro_meds t2d_meds
## 0.05206285 0.21733666## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.192847e+01 -7.132083e-06 2.492549e-04 5.169073e-02 7.814449e-03
## FC_DKTRUE PC1 PC2 PC3 PC4
## -1.338664e-01 3.535628e-01 1.530207e-01 3.951684e-01 6.419134e-02
## htn_meds lipid_meds nitro_meds t2d_meds
## 1.892485e-02 1.269466e-02 -5.044690e-03 1.407807e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.701454266 0.001356990 0.009094404 -0.007569683 0.021153862 0.212490001
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.004907830 2.615364973 1.929285151 -2.186411261 -0.061237147 0.014087446
## nitro_meds t2d_meds
## 0.014622316 -0.177214350## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.861514729 0.013712878 0.006543266 -0.040948255 -0.015776061 -0.394082436
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.677051956 0.439783907 -1.531008293 -0.923181357 0.092370171 0.132518402
## nitro_meds t2d_meds
## 0.055134160 0.124706464## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 8.070535413 0.004527536 0.014402043 -0.211083507 0.007764392
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.227596207 -0.024988277 -2.786779488 -0.459741650 -11.366790861
## htn_meds lipid_meds nitro_meds t2d_meds
## 1.602429111 0.485259194 -0.199196251 -0.252585703## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.853664302 0.009919621 0.008321291 -0.081448340 -0.003031273 -0.062342924
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.544588407 -0.351909968 -0.658902255 -0.773979615 0.074231645 0.055046894
## nitro_meds t2d_meds
## -0.003831893 0.013214577## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.6752304086 -0.0000685656 -0.0015237658 0.0862933758 0.0034803677
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0997210984 0.3841497320 -0.1435866564 -0.8229410935 0.5041475286
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0058064391 0.0563324618 0.0125038053 0.0613185110## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.047157773 0.002366841 0.006039993 0.013347702 -0.009083265 -0.167996306
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.411688653 0.152865922 0.722115680 -0.729392951 0.014886032 0.063134771
## nitro_meds t2d_meds
## 0.052893512 0.096932490## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.961750459 -0.007638908 -0.005695468 0.085361200 0.002766344 0.079913185
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.173775580 -0.025436762 -0.158096225 -0.287075038 0.029453896 0.034711801
## nitro_meds t2d_meds
## -0.006363826 -0.023427370## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.5201579418 0.0088059228 0.0043451885 0.0438939244 -0.0001755126
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.3595441824 -0.8560603915 -0.8536613984 -1.4741133299 -0.3091524154
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0973761717 0.0828839301 -0.0387021485 0.3895163719## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.557241515 0.000816008 0.001714716 0.040965121 0.017675575 0.015504017
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.582928776 0.604267069 -0.111833328 0.551698242 0.062251631 -0.084589887
## nitro_meds t2d_meds
## 0.010617399 0.145976728## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.2458936368 0.0022036198 0.0051774223 -0.0040312248 -0.0051440988
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0460337272 -0.3574872098 -0.6286121534 -0.2511252680 0.4463214361
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0001188369 0.0104862894 0.0021278030 -0.0210705902## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.494450852 0.001950481 -0.002767252 -0.006305614 0.008289436 -0.072082751
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.332641871 0.349837401 0.220429393 -0.676520035 -0.023208203 -0.024281740
## nitro_meds t2d_meds
## 0.018393565 0.016513681## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.6557539904 0.0073048654 0.0032428872 0.1978533614 -0.0007558994
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0468209404 1.4617300685 0.1141243474 0.2559584179 -0.1360567774
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0723703865 0.0500258943 0.0041899615 0.0162718544## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.9589357407 0.0002236441 0.0007055517 0.2392565335 -0.0056073116
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1699605163 1.1056804533 0.3631431695 0.8405623744 -1.0231547363
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0001550094 -0.0064905386 0.0108911787 0.0262451685## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.3832190379 -0.0003115603 0.0007465360 0.0032518530 0.0001208505
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0069236975 0.0434148067 -0.2717028187 -0.4985378735 0.4613922153
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0057032467 0.0038175088 -0.0033647662 -0.0127015722## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 9.84281312 0.01945159 0.01721697 -0.07856328 0.01497058 -0.10095088
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.65652510 0.38936979 1.27554460 -1.61407811 -0.04294493 -0.08917559
## nitro_meds t2d_meds
## 0.10930568 0.15473104## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.164268495 0.013546223 0.005668468 0.050282582 0.004394027 0.458164860
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.834395679 -1.566535621 -1.815735481 1.451390728 0.077907881 0.037520611
## nitro_meds t2d_meds
## -0.002435733 0.385100954## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 9.455658933 0.008880587 0.009492466 0.197542045 -0.012539088 -0.302857111
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.469497167 -0.355275641 1.413790858 1.450128358 -0.073410990 0.193483582
## nitro_meds t2d_meds
## 0.244291870 0.272462169## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.797626045 -0.007221077 -0.006637522 0.081813476 0.003140186 -0.148133058
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.243887322 1.306108640 1.188704839 -0.965038743 -0.007632320 0.029698924
## nitro_meds t2d_meds
## -0.043328789 0.041769231## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.697343333 0.006653145 0.005510534 0.223513766 -0.002541356 -0.133682532
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.318404191 0.082998167 0.192145737 -0.186774832 0.011196071 0.017972316
## nitro_meds t2d_meds
## 0.064677049 0.022091868## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.8358061156 -0.0057847775 0.0024044072 0.0212583844 0.0018923494
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.2126775498 0.0435679022 -0.4673222761 -1.1068581016 0.5460185683
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0394387743 0.0008936793 0.0485276718 -0.1285963184## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.891198314 -0.020090634 -0.014141255 0.075219526 0.036060184 -0.578749308
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 3.439574905 2.156688046 -0.345281501 -0.823269963 0.040934443 0.042591835
## nitro_meds t2d_meds
## -0.059342925 -0.003958792## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 9.5178696547 -0.0000315193 0.0091676838 0.0925746355 0.0026601174
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1647426539 0.3169763905 0.2323853211 0.9495260288 -0.5605795092
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0628397595 0.1610212937 0.1349435138 0.1527060423## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.077128783 0.005338058 0.004521512 0.034032520 0.001707460 0.118402711
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.824036251 0.039469867 0.158179666 0.025151629 0.018997266 -0.005633106
## nitro_meds t2d_meds
## 0.011746748 -0.021752895## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.5881550286 -0.0000051936 -0.0035910008 0.0326604335 0.0055088793
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0106028588 -0.0169097612 -0.2778350570 -1.0841313441 -0.1629111903
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0192390516 0.0304886694 -0.0284719374 0.0864877722## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.197957555 0.018419566 0.021820905 -0.031570173 0.013639734 0.915402986
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.945128536 -0.940371687 -1.758235572 0.461581792 0.003838343 0.054011006
## nitro_meds t2d_meds
## 0.072813481 0.059445962## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.8651876759 -0.0030250883 -0.0009610439 0.0187112467 -0.0001788793
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0226181530 0.0711833142 -0.3272524426 -0.5890615554 0.5842414740
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0251619434 0.0549345285 0.0096856048 0.1283890511## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 17.3106537205 -0.0015705441 -0.0007030478 0.0847521842 0.0004152980
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0535168640 0.0132847794 0.3517551368 0.0953220638 0.2105827544
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0439826582 0.0161935815 -0.0163633795 0.0481814501## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 17.169610987 0.006588925 0.006306308 -0.045473557 -0.008814907 -0.234264704
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -2.219600520 0.151894063 -0.061694691 -1.570722792 0.020475340 -0.010033087
## nitro_meds t2d_meds
## 0.071747164 0.141267317## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.672873042 -0.006398564 -0.004781404 0.074937935 0.002489471 0.083153950
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.206148990 -0.061786983 -0.118166452 -0.084907958 0.024148113 0.029325267
## nitro_meds t2d_meds
## -0.004479405 -0.019608671## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.257786483 -0.005909925 -0.004446633 0.075161904 0.001350373 0.067860145
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.234764565 0.030002708 0.048796660 -0.323606130 0.021772634 0.028538593
## nitro_meds t2d_meds
## 0.003121296 -0.021054610## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.06894841 0.01042554 0.01445491 0.06770575 -0.01007885 -0.02597488
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.54294836 -0.40034014 -0.13650065 -0.18385104 0.03819566 0.03041061
## nitro_meds t2d_meds
## 0.05533896 0.01567844## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.674831622 0.003974051 0.004629492 -0.015506804 0.003092357 -0.066847697
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.753544299 0.888517191 -0.325706814 -0.882342809 0.094394833 0.081818899
## nitro_meds t2d_meds
## 0.018896326 0.128486616## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.1260259490 0.0058178226 0.0053027690 0.0702932575 -0.0033911774
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0985941254 0.4876966066 0.1232064013 1.1635652988 -1.1640628016
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0138075238 -0.0007158398 0.0676290709 0.0650699314## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 17.237380843 -0.001511678 0.001353690 0.070462221 -0.004845096 0.104072851
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.232853741 -0.153432393 0.032847137 -0.401824139 0.034716387 0.004229303
## nitro_meds t2d_meds
## -0.023592228 0.056803030## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.799214602 0.010124549 0.007442291 -0.083970467 -0.003628300 0.110347765
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.852506627 -0.704232272 0.045130565 -0.679889988 0.068546286 0.032367367
## nitro_meds t2d_meds
## -0.005483834 -0.029272976## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 17.660030021 0.002307127 0.000995006 0.056800248 0.002206485 0.031620285
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.121583206 -0.048022782 -0.215662593 0.351393871 -0.032209404 0.002706536
## nitro_meds t2d_meds
## 0.003985814 -0.018289932## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.328632e+01 -1.866332e-04 8.328449e-04 5.203290e-03 3.764728e-04
## FC_DKTRUE PC1 PC2 PC3 PC4
## 3.422191e-02 1.056394e-01 -2.572016e-01 -6.252200e-01 4.641403e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -2.302828e-05 3.780853e-03 -4.856322e-03 -2.833622e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.1507324572 -0.0027530575 -0.0043450876 0.0742548529 -0.0005567243
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0153297567 0.3215348976 0.1200220149 -0.3677163038 -0.1953158497
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0463304885 0.0184670535 -0.0171184517 0.1032405323## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 10.7516282990 0.0001473342 0.0049012961 0.0650676437 -0.0042945111
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0281392305 0.0484969311 0.8242853939 0.3588964785 -0.2870871820
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0380322843 -0.0280272209 0.0464154234 0.0613040472## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.308245e+01 -4.286629e-03 8.050709e-04 1.998334e-02 8.713455e-05
## FC_DKTRUE PC1 PC2 PC3 PC4
## 3.120613e-02 3.606835e-01 -9.042838e-02 -7.041701e-01 1.272690e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -2.248735e-02 5.597297e-06 1.465100e-02 -2.591296e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.805809520 -0.002007617 0.002533627 -0.035451168 -0.007017792 -0.122907736
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.717776291 0.617571106 -0.157560305 0.407297781 -0.014933861 -0.003951521
## nitro_meds t2d_meds
## -0.007026614 -0.044915616## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.322433134 -0.008180788 0.004660074 0.168133030 0.053207670 -0.018788204
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 9.145799564 -4.407054628 0.383476192 4.287145451 -0.077092617 -0.021466811
## nitro_meds t2d_meds
## 0.037108114 0.093553251## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.4644428469 -0.0020256094 0.0003185282 0.0158326406 0.0016752686
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0240111322 0.3304841730 -0.0652787978 -0.1241494670 -0.1272190229
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0059019739 0.0112848839 -0.0026589444 -0.0348525635## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.1473070106 -0.0025940348 -0.0041381576 0.0301089922 -0.0001293177
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0026229886 0.4692012828 -0.2669681836 -0.4414712562 0.5166813320
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0115904442 0.0816175690 -0.0101791071 0.0085267402## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.899861339 0.011281317 0.010171193 -0.005743257 -0.005743506 -0.078405303
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.693682314 -0.196178064 -1.046912947 0.471318275 0.043608982 0.040302667
## nitro_meds t2d_meds
## 0.061901415 0.124364484## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.355637329 -0.003490445 -0.002918177 -0.440813592 -0.004641658 0.005380106
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.128629467 -0.644620804 0.091508708 -0.479372393 0.062551127 -0.021219787
## nitro_meds t2d_meds
## -0.024394683 0.067364125## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.937685632 -0.002440377 -0.002007129 0.066872868 -0.002129488 -0.113378545
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.354321307 0.225954384 -0.585870450 -0.805273404 0.045907633 0.056867274
## nitro_meds t2d_meds
## 0.028539571 0.004067018## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.08588017 -0.01097316 -0.02236515 -0.04881076 0.05186672 -2.26131559
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -3.99681571 2.68045062 -0.56067021 -4.94093337 -0.09426860 0.10708458
## nitro_meds t2d_meds
## 0.05362262 -0.11316997## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.0663837809 -0.0009031501 0.0018856744 0.0100343036 -0.0060345684
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.1075097301 0.5749921013 0.1249374380 -0.1337650269 1.3040480158
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0118541227 -0.0141167919 -0.0166335100 0.0027189791## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.298510369 0.006622537 0.005174502 -0.066925126 -0.010710684 -0.096799289
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.289283164 0.032023593 -1.398010292 -0.098593489 0.081413623 0.139434549
## nitro_meds t2d_meds
## 0.009491769 0.064850671## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.9972040694 0.0021160076 0.0003552212 -0.0220321171 -0.0012539998
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0227874663 -0.2171023888 -0.5795667383 0.3150373426 0.6522449946
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0027003147 -0.0007067813 -0.0308588462 0.0170190900## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.813538166 -0.036617383 -0.001473997 0.325286466 -0.049784698 2.496460832
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -5.801998329 -5.572477159 -3.864902626 1.426953343 0.098295765 0.113328564
## nitro_meds t2d_meds
## 0.010003202 0.208581482## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.807861e+01 9.376259e-04 -3.510457e-05 8.762221e-02 -8.330959e-04
## FC_DKTRUE PC1 PC2 PC3 PC4
## 4.342230e-02 4.095014e-01 -1.409219e+00 -5.153244e-01 6.810570e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## 1.208967e-02 5.613719e-02 2.566902e-02 7.578618e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 17.8946408583 0.0003526078 0.0008693553 -0.0183051680 -0.0021827146
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1044583668 -0.3435936264 -0.2570954315 0.2111687711 0.6352509607
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0166490655 -0.0047118674 -0.0412849958 -0.0018956183## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.391109690 -0.063573698 -0.012010926 0.312742256 -0.040488244 3.049982591
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -7.908353047 -2.433796684 -3.043975708 -4.803308149 0.021138168 0.013617268
## nitro_meds t2d_meds
## 0.005449729 0.245230300## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.397751938 -0.024463757 -0.004818678 0.090051783 0.004774373 0.388101089
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.651177792 -0.074228678 -1.151956621 0.930084501 -0.008285825 0.010604454
## nitro_meds t2d_meds
## -0.024966256 0.017292463## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.547389625 -0.003006414 -0.002206790 0.131599661 -0.006205823 -0.012022724
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.280848810 0.068188909 -0.582601128 -0.808347143 0.087976556 0.042701111
## nitro_meds t2d_meds
## 0.014052328 0.184397884## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.302584593 0.007191477 0.005904462 0.018624536 -0.002575755 -0.056309773
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.426315937 -0.380713418 -0.013321773 -0.067048592 0.016370024 0.013846164
## nitro_meds t2d_meds
## 0.028548205 0.071702259## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.852090e+01 1.121466e-04 6.172806e-04 -1.650305e-03 8.384225e-05
## FC_DKTRUE PC1 PC2 PC3 PC4
## 2.667411e-02 1.700999e-01 -2.216640e-01 -5.306116e-01 2.760349e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -5.224306e-03 1.512092e-03 -4.297666e-03 -1.842572e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.672478079 0.014033698 0.004225106 0.014844404 0.010082782 -0.331499584
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.226362111 0.762286775 -0.467051068 0.923530230 0.111844742 0.041958315
## nitro_meds t2d_meds
## 0.028335515 0.073988124## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 20.5275043869 0.0000624254 0.0004094289 -0.0021542925 -0.0003323003
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0043966677 0.0683002713 -0.2125303549 -0.4470506418 0.2342128860
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0036472957 0.0026136059 -0.0001982752 -0.0113298958## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.565796664 0.008376511 0.005852123 0.060138804 0.003566217 -0.066856479
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.562495464 -0.441730233 -0.450526298 -0.103445403 0.043199233 0.030352531
## nitro_meds t2d_meds
## 0.028985616 -0.011608752## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 17.0752700243 -0.0011017791 -0.0047762595 0.1766742667 -0.0030818727
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0094534669 0.7626974848 0.0581878382 -0.6008565700 0.1366782564
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0604536694 0.0327782755 0.0004630838 0.1790968089## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.951417522 0.002106161 -0.018001214 0.244355221 0.006341566 0.105667789
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.786561414 -5.061217025 -0.501946447 0.424917809 0.095759598 0.152207727
## nitro_meds t2d_meds
## 0.008920737 -0.072830027## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.176452583 -0.005889919 -0.011881343 0.109341402 -0.006649476 -0.498897469
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.424021618 0.111261232 -0.505196155 1.470811418 0.003414858 0.038700660
## nitro_meds t2d_meds
## -0.023329407 0.043168449## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.766586767 -0.002432804 -0.001006029 0.003451984 0.002189516 0.112455947
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.042260070 -0.823755701 -0.838638284 0.314504523 0.027299243 0.015725170
## nitro_meds t2d_meds
## 0.003464260 0.011878392## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 7.704913981 0.007810297 0.020919503 0.180753017 -0.012089980 0.252442435
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -4.114353780 1.249201382 -1.081424561 -0.610919564 -0.020720496 -0.012731530
## nitro_meds t2d_meds
## 0.021583004 -0.026289595## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.306142963 -0.001745557 -0.003057858 0.161431983 -0.001354064 -0.002019686
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.796052803 -0.579291618 -0.353994665 0.430477040 -0.022144201 -0.006389624
## nitro_meds t2d_meds
## -0.011702744 -0.141690603## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.4738381684 0.0031536584 -0.0024864590 0.0225618209 0.0009810741
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1449034038 -0.0189978808 -0.3562864690 -0.6664646822 0.3489868678
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0035933807 0.0376654766 0.0153586473 -0.0016131566## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 10.3863127896 0.0007725663 0.0031190496 0.2871038259 0.0063023306
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0319346257 1.9884172573 0.3761182440 -1.3657981355 1.6537609750
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0130874706 0.0290086061 0.0324810223 -0.0498687511## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.834647939 -0.011498313 0.005318438 -0.019421317 -0.006655469 0.346310139
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 3.465945927 -2.078581104 -2.215942837 4.681265393 -0.029704630 0.002455090
## nitro_meds t2d_meds
## -0.032644218 -0.113586850## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 17.737574851 -0.002391868 -0.005746108 0.126054327 -0.003016029 0.016508678
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.568966164 -0.126278038 -0.188811453 0.104879710 0.047296029 0.013374557
## nitro_meds t2d_meds
## -0.027692250 0.140257797## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.297357114 0.001208565 -0.009264038 -0.011865453 0.021126415 0.758924090
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.216513097 0.262724987 -3.216280147 2.031359151 0.164797959 0.050187213
## nitro_meds t2d_meds
## -0.022997925 -0.139771714## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 8.836976734 0.021398411 0.033501669 0.108564478 -0.023305916 0.107003033
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.435976379 -2.000048826 -1.310838476 1.310976271 -0.039681831 0.003218401
## nitro_meds t2d_meds
## 0.155127719 0.189072283## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.0316360371 -0.0249699816 -0.0010276531 0.1084093002 0.0004992806
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0765489916 3.2857386108 -0.4661756530 -1.9536149069 -0.1025179343
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0425788680 0.0043109443 0.0441368565 -0.1302998802## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.193151876 0.005776935 0.007741977 0.012917391 -0.003849510 -0.056082276
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.328598046 0.219009878 0.509885432 0.449740810 -0.022092229 0.031888979
## nitro_meds t2d_meds
## 0.037090195 -0.115504744## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.414075293 0.006687704 -0.001213167 0.168096184 0.005772377 0.131363457
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.476656236 0.085175959 0.257420725 -0.239322157 0.021470139 -0.020092685
## nitro_meds t2d_meds
## 0.041907178 0.036791198## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.818517389 0.001926891 0.014048954 -0.055781147 0.029056839 -0.410698788
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.295220817 2.765857207 1.504494551 -0.525405244 -0.022831249 0.026748864
## nitro_meds t2d_meds
## 0.020343346 0.106695272## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.583690741 0.009150737 0.006748784 -0.157914414 0.000656173 -0.096482086
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.937388735 0.503484524 -0.098974404 -1.799326059 -0.077453936 -0.082830465
## nitro_meds t2d_meds
## -0.012231786 -0.007242649## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.559866250 0.007268839 0.002152069 -0.006904652 -0.006063042 0.005881898
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.444302403 -0.222307197 -0.521878684 -0.424140653 0.040275547 0.065970630
## nitro_meds t2d_meds
## 0.010313249 0.105917115## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.442778e+01 2.244654e-04 7.004864e-05 -4.003773e-03 -2.036192e-04
## FC_DKTRUE PC1 PC2 PC3 PC4
## -9.092400e-02 4.931017e-01 2.443185e-01 3.401030e-02 4.943041e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -6.741025e-03 -1.211049e-02 -1.415400e-02 -2.776429e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 17.7255870091 0.0024070194 0.0013737984 -0.0491352915 -0.0009827403
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0794435118 -0.4852033253 -0.2216275283 -0.2242098636 0.3885736530
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0013283578 -0.0195376383 -0.0036766869 -0.0339012077## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.659448134 -0.000180332 -0.005085725 0.053404495 0.004347661 0.150580610
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.253930281 1.517371081 0.895172758 -1.190999436 -0.031355712 0.018044838
## nitro_meds t2d_meds
## 0.034756392 0.019468193## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.572141016 -0.008453315 -0.004559487 0.030898939 -0.002366855 -0.442761471
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.132439916 0.493961980 0.772321369 0.341964325 0.021521378 0.008889102
## nitro_meds t2d_meds
## 0.019962905 0.024002319## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.7267051853 0.0005997602 0.0014185849 -0.0078929366 0.0004556454
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0171735089 -0.4811202196 -0.3017987861 -0.2393374167 -0.2089886993
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0045172103 -0.0149035440 -0.0081992462 -0.0136800192## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.892840801 -0.001342786 0.018177461 0.096613512 0.002784272 0.483547856
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.672138538 -1.498462627 -0.179344552 0.675686149 0.029496077 -0.006172630
## nitro_meds t2d_meds
## 0.019473610 0.257056748## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.999801511 -0.004088963 0.001888886 -0.112361241 -0.004331362 -0.362886406
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.947989045 -0.572085449 -0.039613793 -0.458926145 -0.002016553 -0.034423526
## nitro_meds t2d_meds
## 0.003011194 0.003922762## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.9949075107 -0.0122833225 -0.0060270368 -0.0157499067 -0.0002508356
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.5420605184 -0.3034874613 2.0245902171 1.2918787016 -1.1361760205
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0013484044 -0.0084913321 -0.0037026453 0.0873613119## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.669870781 -0.005666317 -0.002728270 0.003928235 -0.001501621 -0.126905160
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.227683812 1.248908584 2.399864446 -1.333882440 0.008439328 -0.031386305
## nitro_meds t2d_meds
## 0.049468188 0.050623189## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.2725581149 -0.0040675142 -0.0037513391 0.0499143985 0.0034238317
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1729336718 0.5755009505 0.6191953349 0.4360750629 -1.0317077710
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0386377874 0.0005229193 -0.0126686674 0.0773527280## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.607667350 0.002640840 -0.002688514 0.027767781 0.003353699 -0.006853819
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.167591554 -0.212806811 -0.602766058 0.571753981 -0.015871125 0.017263704
## nitro_meds t2d_meds
## 0.011054885 0.005187794## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.219252137 0.005325262 0.014483863 0.085473387 -0.002589814 0.169312806
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.118737917 0.203253867 -0.892965156 -0.932685742 -0.013980580 -0.014767701
## nitro_meds t2d_meds
## 0.105442570 0.163739221## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.680271679 -0.009960056 -0.007506960 0.220389699 0.007960016 -0.132345741
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.737354681 0.973591997 0.284624583 -0.706553902 0.051825038 0.014552187
## nitro_meds t2d_meds
## -0.012561759 0.133260578## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.241853304 -0.010950525 -0.005738808 0.368210434 -0.019368001 -1.614139189
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -2.614520039 -3.738346113 -2.865834854 3.002810691 0.130704736 0.104629784
## nitro_meds t2d_meds
## 0.027946786 -0.107472279## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.586399402 0.013628133 0.007115201 0.039088095 0.002383734 0.330614393
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.011486621 -0.988804659 -1.303109243 -0.015558048 0.058827161 0.037861688
## nitro_meds t2d_meds
## -0.002847411 0.376431656## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.794851518 0.002906584 0.001515940 0.052827228 0.007547556 -0.235503570
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 3.478117497 -2.098085857 -0.791622705 1.116433654 0.043267890 -0.014852533
## nitro_meds t2d_meds
## -0.028072374 0.285179536## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.6197654004 -0.0019863802 -0.0007809079 -0.0042966363 0.0049979430
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1761293576 0.7731843740 0.4583887464 0.1833976416 0.4080837528
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0074262762 -0.0098947991 -0.0016944911 -0.0128072881## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.433929595 0.002897596 -0.001427892 -0.006326853 0.001285215 -0.027114626
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.224059188 0.426385825 0.281252339 0.585897164 0.014470885 0.013906061
## nitro_meds t2d_meds
## -0.025287640 -0.003075893## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.6269863797 0.0004559694 -0.0030844247 0.0279485999 0.0003089077
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.3874165354 -0.1592989462 0.0998944824 0.2612812029 -0.8970767064
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0407256939 0.0407744011 0.0256619722 0.0524959685## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.1809111085 -0.0001639661 -0.0037005822 -0.0238389097 -0.0002496378
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0394842300 -0.2317347380 -0.4954639477 -0.5438355216 -0.0757709639
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0262257222 0.0275372211 -0.0099913251 0.0442229890## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.899445310 -0.006291441 0.007853501 0.010457052 -0.008127233 0.132811653
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.084224225 -0.062370654 0.162970765 -0.467482470 0.016249095 0.008306209
## nitro_meds t2d_meds
## 0.064029507 0.090264569## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.882673253 -0.006579015 -0.008230568 0.100800109 -0.005647232 -0.051286344
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.539832958 -0.025693505 -0.360278040 0.921447237 0.013397175 0.033350736
## nitro_meds t2d_meds
## 0.040125321 -0.270380671## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.83951446 0.03320757 0.02073965 0.09537063 0.01385220 0.19318806
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.40814135 0.13807663 -0.22315580 0.05814670 0.06509810 0.10572490
## nitro_meds t2d_meds
## 0.44949814 0.16189398## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.316956797 -0.002491751 -0.003374700 -0.056721072 -0.002931554 -0.352270752
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.000191416 0.668801117 -0.531552182 0.028070854 0.029508230 0.080174099
## nitro_meds t2d_meds
## 0.035425356 0.001081332## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.4094505858 -0.0028089498 0.0099183212 0.0951945988 0.0071704133
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.2361040821 0.6405955335 -1.2016231493 0.8956243229 0.3872815848
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0380989179 0.0009522764 -0.0264922335 0.0156411519## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.6626367230 -0.0041834637 -0.0005134659 -0.0837922937 -0.0050851620
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.5056039249 -1.4598341482 -0.4447120508 0.5154970882 -0.4899361376
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0034761060 -0.0209522686 -0.0090796872 -0.0204102117## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.163233346 0.027369661 0.011144708 0.270014282 0.005670108 -0.823188964
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.072584788 2.837791734 3.918340655 -4.213250934 0.211887052 0.538395678
## nitro_meds t2d_meds
## 0.061649137 0.280341920## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.6656737482 -0.0005256656 0.0004831619 -0.0042713024 -0.0037938134
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1154145440 0.0779019058 -0.3500818136 0.3078182142 0.7296050595
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0024109606 -0.0191618878 -0.0073004816 0.0342687110## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.674098945 0.005903669 0.003585387 0.068030076 0.004116737 -0.371265495
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.657282038 0.305845917 0.946623245 -1.526199667 0.117052322 0.149741637
## nitro_meds t2d_meds
## 0.018223173 0.211671027## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.284750459 0.007785889 0.008582620 0.085025643 -0.002844990 -0.149667191
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.526204962 -0.688481105 0.237770372 -0.459699202 0.042980644 0.025902588
## nitro_meds t2d_meds
## 0.034448176 0.087836467## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.3358065624 -0.0059339086 -0.0008388358 -0.0152483579 0.0162632273
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.9362128702 -0.0740373196 0.4701564924 -0.1005257771 -0.2288705169
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0509003227 0.0612034278 0.0691136926 -0.1128426719## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.885873070 -0.003207514 -0.000928357 0.052716585 -0.006777319 -0.220503493
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.228876279 -0.285566351 0.324755770 -0.130450406 0.041966685 0.051243188
## nitro_meds t2d_meds
## 0.028504892 0.042939160## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.110635285 0.001410142 0.007286849 0.062444288 -0.003439114 0.133767596
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.325075119 -0.943423396 -1.516978192 -0.419609883 0.039922354 0.061552590
## nitro_meds t2d_meds
## 0.019417624 0.020322779## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.436750360 -0.011847665 -0.000474324 -0.063899667 -0.002346166 0.281844610
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.974128711 0.296590964 0.242741980 -0.019503123 -0.003963232 -0.015314888
## nitro_meds t2d_meds
## 0.030733943 -0.022212538## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.352578979 0.010402696 0.004876116 0.043246472 0.027708471 0.388686827
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 3.407518453 1.270835276 0.746716154 0.796092903 -0.034624742 -0.079991168
## nitro_meds t2d_meds
## 0.019917283 -0.011728897## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.9730346567 -0.0005698244 -0.0010351903 0.0070661780 0.0042758070
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0074991289 0.1590992913 -0.2078347055 -0.4491927667 0.4875840258
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0186957147 0.0246284635 -0.0141073143 -0.0139704493## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.397085483 0.002695448 -0.003263484 -0.079088302 0.009257697 -1.318488397
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.537848290 1.313485072 2.860515306 -0.349991092 -0.016418903 -0.018974654
## nitro_meds t2d_meds
## 0.008589386 -0.091617120## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.463336601 -0.028950141 -0.034342213 0.376648426 -0.009950596 0.128963512
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.922760723 -3.174150858 -2.202732889 1.913198643 0.026175619 -0.176655379
## nitro_meds t2d_meds
## -0.041993080 -0.043010042## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.6722040021 0.0106595600 0.0023167197 -0.2445965645 0.0007705885
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1738593437 -0.9517496086 0.1051326505 0.4488674227 -0.7932353561
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0168855750 -0.0589333156 0.0114401954 -0.0236062383## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.403900686 -0.012879957 -0.008626914 0.061518160 0.010876397 -0.616334272
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.275007350 -1.807166418 0.879354628 0.939627547 0.980399886 0.064668416
## nitro_meds t2d_meds
## -0.257688471 0.050080875## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.4093163948 0.0047841965 -0.0007912307 0.0517602490 -0.0043469614
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1346616003 -1.4189651421 0.2717087061 -0.0885293555 -1.9321905122
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0583043129 -0.0229740415 0.0078710001 0.3025930116## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.566719776 0.019166573 0.019732864 0.129543308 -0.008840624 0.636520194
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.493351516 0.410889750 -1.223164718 0.105581394 0.165927518 0.037247917
## nitro_meds t2d_meds
## -0.086594034 0.019830424## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.6832231965 0.0009242744 0.0009232243 -0.0811922414 0.0130124787
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.2287764785 1.1275363897 0.5439647319 -0.0587305504 0.5548080541
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0244292903 0.0232771255 0.0191862244 -0.0462146489## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.208777097 0.006104257 -0.003616861 -0.060898764 0.013448482 -0.518266308
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.086585341 0.584938572 -0.009995869 -1.330051903 0.023407308 0.058522157
## nitro_meds t2d_meds
## 0.001388218 -0.069848854## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.8028733738 0.0012065696 -0.0002292434 -0.0196716969 -0.0005989771
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0891419051 -0.0449680627 -0.1539257476 -0.1192061965 0.8459346409
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0016114658 -0.0101052024 0.0022025539 0.0105606388## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.0557109177 -0.0025620933 0.0032620631 0.0716450151 0.0007529775
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0461905532 -0.0424593251 0.1709683035 -0.4997871774 0.3135353941
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0102206845 -0.0268389607 0.0081075250 0.0121967081## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.973860412 0.014616947 0.016514410 -0.088404898 0.005289611 -0.078088717
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.049924392 1.129220260 0.432490565 0.468924556 -0.052293164 0.041417776
## nitro_meds t2d_meds
## 0.071468473 0.037861934## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 19.060509182 0.002791811 0.002972270 -0.212481651 -0.002056926 -0.659814567
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -2.551815286 0.007943152 0.407334817 -1.556098529 0.031342986 -0.056547156
## nitro_meds t2d_meds
## 0.007922838 0.059874020## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.7114086824 -0.0065518344 0.0130363879 -0.0133834051 0.0981047462
## FC_DKTRUE PC1 PC2 PC3 PC4
## 1.6807620338 8.9129988627 2.3895054996 0.5571648171 1.5461758112
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0322314435 0.1728759056 -0.0003880965 -0.1500498621## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.157231412 -0.001012443 -0.007258209 0.187665306 -0.003249619 0.242607552
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.711458914 -0.182550634 -0.640375053 0.158928147 0.062092051 -0.030820953
## nitro_meds t2d_meds
## -0.009295757 0.127707166## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.761715138 -0.006748097 -0.004758479 0.087558918 -0.003895085 -0.024204285
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.501480679 0.098819924 0.312796611 -0.267707910 0.069506726 0.061445094
## nitro_meds t2d_meds
## 0.043655874 0.119100596## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.1274440920 -0.0007183214 -0.0039039076 0.0834457270 0.0055067884
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.5225289292 0.4662250869 0.1995350219 0.2065359935 -1.2223245673
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0047860536 0.0078492909 0.0389779119 0.1312462793## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.5809863082 0.0056928679 0.0052004215 0.0478958433 0.0036172247
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0779353338 0.4177455283 -0.0745920945 -0.5297392847 0.4657904980
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0005055282 0.0184121180 0.0207569954 0.0540955293## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 20.9941573380 -0.0007731194 -0.0003242943 0.0046803501 0.0012192605
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0288911347 -0.0389400311 -0.1990701435 -0.3654606160 0.3371084145
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0173625050 0.0137137183 -0.0124564010 -0.0184235086## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.3761978930 0.0021702906 0.0005490078 0.0019287475 -0.0019957092
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1140507148 -0.2129553545 -0.0142025246 0.0369904035 0.3379285813
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0036947980 -0.0152303100 -0.0095338457 0.0083701137## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.416418738 0.001326290 0.001171805 0.122678925 -0.001794347 0.035081842
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.031959435 -0.204621431 -0.725570433 -0.096484784 0.057013067 0.008268272
## nitro_meds t2d_meds
## 0.017756693 0.005832517## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.9507648141 0.0036870369 -0.0005237824 -0.0219848637 -0.0011229493
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.4021890776 -1.1208644438 -0.6822009681 1.1747324211 -0.4548034861
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0726470434 -0.0358146299 -0.0252138267 -0.0306515220## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.9334157282 0.0044332303 0.0029862166 0.0002012644 0.0173070567
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.1301795917 0.8701957214 0.3805327443 -0.8961449618 -0.2887620959
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0339472062 0.0333167912 0.0296692547 -0.0315156092## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.267570610 0.009455019 0.008872780 0.041656509 0.001028598 0.037916913
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.583958519 -0.358812044 -1.060561012 0.485183026 0.029569799 0.072100232
## nitro_meds t2d_meds
## -0.009775097 0.112270307## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.1021990722 0.0062293644 0.0054050269 -0.0087670308 0.0061450958
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0423455568 -0.1077757907 0.6652472982 -0.0547606034 0.2531875866
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0012283203 0.0002155707 0.0200862073 0.1016509478## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.952806955 0.006254681 0.008495188 0.238218712 0.013268262 -0.660733392
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.402527994 2.475338392 3.661506464 -2.927181379 0.194119325 0.474656749
## nitro_meds t2d_meds
## 0.084274433 0.133827552## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.034527559 0.009266462 0.008094931 -0.023545390 0.001279678 -0.068801192
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.030860629 0.882710403 0.093309011 0.462586652 -0.065170434 0.006848104
## nitro_meds t2d_meds
## 0.090863321 0.047083455## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.6525358369 -0.0009413305 0.0009355803 -0.0438632191 -0.0018380046
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1967307448 -0.2162184630 0.1140967206 0.7691812779 0.4995569408
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0060615275 0.0058194941 -0.0121865199 0.0196988325## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.059749194 0.001101333 -0.001606385 -0.098642671 -0.003847487 0.028948293
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.458441120 -0.477475789 0.042117209 -0.540847656 0.053869826 0.029733761
## nitro_meds t2d_meds
## -0.009670134 0.045844140## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.7779103887 -0.0134587038 0.0026652831 0.1196077827 -0.0004318594
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0492678878 2.2359938228 -0.6905908495 -1.2654297903 -0.6024614278
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0188325136 0.0034036162 0.0281214517 -0.0296454387## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.193516477 0.007490240 0.009763061 -0.182517187 0.008562206 -0.379230599
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.196367644 0.150326609 -0.205998507 -0.971094867 0.038400296 0.019891229
## nitro_meds t2d_meds
## 0.045937568 0.107074251## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.256191541 0.004144205 0.005588710 0.059661856 0.003547509 0.194640695
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.593084604 0.734515099 0.050506197 -0.512022928 0.032461243 0.043663121
## nitro_meds t2d_meds
## 0.020123742 -0.037354724## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.1143264355 -0.0030868123 0.0005741783 0.0566552911 -0.0004349666
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.3001629928 -0.3433605116 0.5934319964 0.2553150637 -0.7600443238
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0035894571 -0.0187098347 0.0477628858 0.1473251558## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.352224e+01 4.462092e-03 -4.478713e-05 9.544383e-03 9.636447e-03
## FC_DKTRUE PC1 PC2 PC3 PC4
## 5.575206e-01 1.707856e+00 6.289462e-01 -4.117327e-01 1.144587e+00
## htn_meds lipid_meds nitro_meds t2d_meds
## 2.302439e-02 -5.856379e-03 2.428501e-02 -1.501646e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.624690839 0.009538450 0.004660518 0.136683960 -0.008686146 0.856343475
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.423931673 -0.948347438 0.057561685 2.349410846 -0.032898927 -0.045550429
## nitro_meds t2d_meds
## 0.009027933 0.071391523## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.746747826 0.007048568 0.007619296 -0.007679605 -0.007799414 0.048439312
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.422314355 -0.341160060 -0.030814592 0.509444366 0.028602377 -0.048203910
## nitro_meds t2d_meds
## 0.018471489 0.036746052## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.61126404 0.01816403 0.01997182 0.03649806 0.03161903 0.98614401
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 2.69748872 2.22025958 2.52700721 -0.73918851 -0.07346482 -0.08149553
## nitro_meds t2d_meds
## 0.02601996 0.01929496## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.9280337482 0.0040034671 0.0063871859 0.1942126396 -0.0073511020
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0712266017 0.3747793257 -0.6333121214 -0.2345191231 0.3321461350
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0319887865 -0.0009141139 0.0667632590 0.0393668083## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.2900835848 -0.0058159722 0.0001309569 -0.0388372990 0.0010041687
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1140671882 0.5513291185 -0.0454748169 0.0368514332 0.2717989246
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0078119449 -0.0067547833 0.0206463427 -0.0428361033## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.945926e+01 5.774490e-03 3.782505e-03 -2.135446e-01 7.693365e-06
## FC_DKTRUE PC1 PC2 PC3 PC4
## -7.074380e-02 -2.791462e+00 -3.281029e-01 4.501348e-01 -1.682230e+00
## htn_meds lipid_meds nitro_meds t2d_meds
## 3.342087e-02 -2.753862e-02 -8.283499e-03 7.221452e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.697502474 0.005636143 0.012663609 -0.052989097 -0.001224681 -0.096493205
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.371522407 -0.211784330 0.334965882 -0.875079722 0.020332608 -0.012912799
## nitro_meds t2d_meds
## 0.050896677 0.245401397## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 10.328109122 -0.006259570 0.006036890 0.144318563 -0.005867261 0.310918954
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.549968382 -0.801805907 0.918393458 0.615085426 -0.000057604 -0.033440249
## nitro_meds t2d_meds
## -0.009754977 0.065820721## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.4177920307 0.0077793797 0.0036226347 -0.0141256764 -0.0003924469
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.2399716815 -0.3125501610 0.3164649065 -0.0842526262 -0.8317088707
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0064688975 -0.0031124423 0.0354434713 0.1716240326## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.1474358289 -0.0004632423 0.0011027313 -0.0085428827 0.0033087029
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0017848706 -0.5098146045 -0.4563452189 -0.6224989055 -0.1307030958
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0106277706 -0.0062191066 -0.0183725991 -0.0225526043## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.041036890 -0.002181489 -0.007676067 0.280724560 -0.002961213 0.186084917
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.602131149 -1.276633157 -0.935026738 0.235169972 -0.009758134 -0.045096887
## nitro_meds t2d_meds
## -0.016130561 -0.001126237## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.65721324 0.01307704 0.01166384 -0.09674429 0.03295863 -0.06759773
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.48038781 0.27624933 0.42512363 1.40723767 0.01330593 -0.05431784
## nitro_meds t2d_meds
## 0.02224398 0.01583034## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.202966989 0.009279404 0.012565806 0.012195809 -0.007259825 -0.062047603
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.138307626 -0.737567521 0.183206535 0.210855867 0.029959088 0.024153252
## nitro_meds t2d_meds
## 0.066323996 0.125327019## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.996417161 0.011911383 0.006264757 -0.074416594 0.016528330 0.260001748
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.009339512 1.547626946 -1.087184401 1.627919450 -0.046889846 -0.023831385
## nitro_meds t2d_meds
## 0.061599330 0.063344755## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.0140662149 0.0029031971 0.0029814151 0.0039166159 0.0009723594
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0596771817 -0.2261321619 -0.3041879829 -0.0391019313 -0.4906638355
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0048887760 0.0057926350 -0.0073729071 0.0232819462## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.523707871 -0.004735796 -0.000352444 0.000564320 0.002769619 0.117074625
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.955857360 -1.047598837 -0.693995747 1.001662197 -0.010830785 0.014722319
## nitro_meds t2d_meds
## -0.006225074 -0.043689219## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.328797e+01 -8.676715e-03 -3.462155e-03 6.597449e-02 -7.446616e-03
## FC_DKTRUE PC1 PC2 PC3 PC4
## -2.041669e-01 -5.714783e-02 -6.392473e-01 -5.352166e-01 1.170820e+00
## htn_meds lipid_meds nitro_meds t2d_meds
## 9.634408e-03 -6.570631e-05 -1.306754e-02 -2.528314e-03## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.6714805918 0.0140166813 0.0130180781 0.0016727454 0.0021223563
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0701156656 0.7946839090 -0.6112704166 0.1351756545 0.0266016345
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0143954959 0.0008657391 0.0886906608 0.1594761715## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.278680842 0.027497165 0.005228477 -0.079815887 0.020094956 0.511670828
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 3.613012533 -0.212691704 -0.591586229 -1.686653961 -0.030235402 0.044568535
## nitro_meds t2d_meds
## 0.038449042 -0.035916919## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.352258553 -0.011951240 0.002860219 0.066692913 0.001561498 -0.161063835
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.884378170 2.134166961 -3.681039016 -0.031681599 -0.066768079 -0.027736734
## nitro_meds t2d_meds
## 0.105856652 -0.167645051## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.742074182 0.014965754 0.001688377 0.134636494 0.028426229 -0.046940459
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.469803752 -0.122551456 -0.749353280 0.092689381 -0.005658647 0.020121290
## nitro_meds t2d_meds
## -0.004457891 -0.088855545## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.226741626 0.016475809 0.013333920 0.083091454 0.002074383 -0.052499748
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.800970744 -0.200005576 0.266002808 -0.342151494 -0.020906307 0.002662944
## nitro_meds t2d_meds
## 0.078011253 0.081998478## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.998700902 -0.002213916 -0.003212911 -0.014874248 -0.001966807 -0.086124526
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.863593837 0.093900171 -0.548198179 1.731268674 0.010599222 -0.007984298
## nitro_meds t2d_meds
## 0.021981140 -0.027082529## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 16.3945195494 0.0010634905 0.0010609345 -0.0132754512 -0.0006455213
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0191429294 -0.2666628698 -0.0844124774 -0.2021458080 0.3132946926
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0015289167 -0.0071074750 -0.0077170204 -0.0313370121## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.459279696 -0.004829474 -0.001168242 0.022744114 0.004733817 0.043817637
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.920702718 -0.255490399 -0.809072297 -0.186718041 -0.009919005 -0.006836616
## nitro_meds t2d_meds
## 0.005178948 -0.002238515## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 18.227418257 -0.008751412 -0.002654281 0.100925904 -0.002801980 -0.440934688
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.040681503 0.896901027 0.628020333 -0.670503403 0.015239619 0.002620504
## nitro_meds t2d_meds
## 0.006289558 -0.065048528## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 11.4125836841 -0.0019894009 0.0009529088 0.0176094640 0.0007417648
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0425839905 0.5904309214 0.2309663385 0.4549907241 0.3577654796
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0012362731 -0.0157938457 -0.0085646960 0.0006889556## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 14.308007198 0.026390555 0.022424919 -0.113854429 -0.003774761 0.124758141
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.489979464 0.932964964 -0.864400282 -0.679580201 -0.062349800 0.046485208
## nitro_meds t2d_meds
## 0.067282450 0.195133858## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.8406977074 0.0068575593 0.0027539919 0.0508698759 -0.0061466823
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.1207813116 -1.6388712284 0.2020155755 0.1191210782 -1.7459066282
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0284511053 -0.0516668854 -0.0009601813 0.2580646067## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 13.60471596 0.01034998 0.00247420 -0.02809258 0.01481661 0.60367050
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.47361282 0.83550200 -1.24079106 1.35941859 0.09484509 0.03590414
## nitro_meds t2d_meds
## 0.02214967 -0.01864003## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.772645989 0.004898189 0.010488072 0.032564543 0.004455523 0.198500916
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.635019940 0.096153356 0.893508173 -0.194180980 0.001026741 -0.013178607
## nitro_meds t2d_meds
## 0.054230279 -0.018976276## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.072848109 -0.003488888 -0.002585792 -0.006888433 -0.002002579 0.040787922
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.675153234 -0.342142871 -1.113580804 1.445248944 0.011093487 0.014348577
## nitro_meds t2d_meds
## 0.022410620 -0.028108290## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.1995930168 -0.0044488170 -0.0028013889 0.0008977923 -0.0029324310
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0725516130 0.2448268981 -0.3677852926 -0.2421984487 -0.1121141619
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0388276549 0.0041513538 -0.0052426661 -0.0365518278## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.3261103949 0.0084314547 0.0028934482 -0.0123727993 0.0015545669
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0001321374 0.6989594594 0.3050779539 0.0720371792 -0.9989352287
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0229612064 -0.0051733253 0.0538601926 -0.0068718942## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 13.2607805524 -0.0002836392 0.0036684643 0.0009995075 0.0017762315
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0376832712 0.5109119354 -0.0968473521 -0.2911383966 0.2708530941
## htn_meds lipid_meds nitro_meds t2d_meds
## -0.0148083297 0.0087901455 0.0074907931 0.0107018865## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 19.504173353 -0.008500535 -0.003571552 0.045581002 -0.009045207 -0.481479879
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.265514830 0.674495493 1.244714706 0.247548760 0.033933019 -0.002933456
## nitro_meds t2d_meds
## 0.022189449 0.021150017## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 15.6024061978 -0.1379932502 0.0010047251 0.0194038432 0.0076545992
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.6552141074 0.8650791350 -1.6276210867 -0.2490175185 -0.0001662132
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0485700212 0.1683352733 -0.0140805569 -0.0708872479## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.048221511 -0.005199502 -0.003808401 0.166665169 -0.004322494 -0.026454570
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 0.700200358 -0.094006443 -0.469767258 -0.065444190 0.035021819 -0.001711970
## nitro_meds t2d_meds
## -0.008726316 0.100954698## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.283079904 0.014600851 0.005254195 0.022858045 0.004449699 0.567170510
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.321898478 -0.386561842 -2.166931640 1.610242839 0.107165722 0.072354080
## nitro_meds t2d_meds
## -0.056888575 0.454904087## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 12.8809133930 0.0018158905 0.0061984660 -0.2652743779 -0.0145260809
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.4006493136 -0.7218787375 -0.7414381499 -0.6150688869 0.4026462893
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0327199776 0.0009560413 0.0183240031 0.1154776957## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.3919241546 0.0003848655 0.0004158978 -0.0171626554 -0.0004613894
## FC_DKTRUE PC1 PC2 PC3 PC4
## -0.0825654134 -0.3853300427 0.0526024667 0.0628773905 0.1626343214
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0010250827 -0.0180217143 -0.0057099345 -0.0006638781## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 20.4631588896 0.0004078203 0.0003804524 -0.0096315253 -0.0005303364
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0320078990 -0.3189441922 -0.1693456813 -0.2923186433 0.2617862350
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0110466826 0.0027481221 -0.0042065277 -0.0204303884## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.350417095 0.009415358 -0.010935374 -0.178557688 -0.004191069 -0.785984772
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -1.653636529 -3.299238878 4.369810436 -4.760729603 -0.078701934 0.122486077
## nitro_meds t2d_meds
## 0.278840908 0.604293363## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 17.870975045 0.005257604 0.003015923 -0.002878465 -0.002424840 -0.104041388
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.170912526 0.450142492 0.400845174 -0.901894697 -0.004794941 -0.017338254
## nitro_meds t2d_meds
## 0.010987666 0.115632848## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.452091e+01 -2.886010e-03 -6.710236e-05 1.387576e-02 5.989235e-04
## FC_DKTRUE PC1 PC2 PC3 PC4
## -2.508443e-03 7.978785e-02 3.619866e-01 -1.069625e-01 3.104745e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -2.995198e-02 1.653328e-02 9.215364e-03 3.742656e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 16.580376347 -0.002806289 -0.004344457 0.038620189 0.004285286 -0.054664693
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.246222633 -0.752447573 -0.589043903 0.648843498 0.006130561 -0.001682803
## nitro_meds t2d_meds
## -0.014960653 0.046020914## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 14.5604466680 -0.0053401653 -0.0010078645 -0.3959965940 -0.0050254202
## FC_DKTRUE PC1 PC2 PC3 PC4
## 0.0080765157 1.0305414528 -0.6383909403 0.0009346226 0.1079505417
## htn_meds lipid_meds nitro_meds t2d_meds
## 0.0880454246 0.0065926570 -0.0270261305 0.0808102886## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 11.98220835 0.00819838 0.01020543 -0.02343024 0.01958597 -0.01571246
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## 1.14322802 1.31300833 0.30818410 -1.02386045 0.00658681 -0.04839958
## nitro_meds t2d_meds
## 0.04370292 0.04929267## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 15.140984318 0.013389248 0.005240920 0.125612418 -0.021940894 -0.220015729
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.740191637 -0.499664495 -0.122965332 1.187985182 0.142022161 -0.106206661
## nitro_meds t2d_meds
## 0.004290152 0.214685216## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.686345060 0.002138627 -0.020802364 -0.007205768 0.029667219 -0.558579057
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.191806659 0.975063267 0.209953365 -2.649007529 0.007587507 0.025712863
## nitro_meds t2d_meds
## -0.017538549 0.255578154## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education
## 1.370377e+01 8.830158e-03 4.826548e-03 -6.604402e-03 9.659358e-05
## FC_DKTRUE PC1 PC2 PC3 PC4
## -6.125754e-02 4.800594e-01 -3.942969e-01 -6.371963e-01 -2.765378e-01
## htn_meds lipid_meds nitro_meds t2d_meds
## -2.947329e-02 -4.390119e-03 3.529447e-02 -4.866873e-02## Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
## running glm to get initial regression estimate## (Intercept) delta.t Age.e SexMale Education FC_DKTRUE
## 12.711109325 0.008225954 0.003195425 0.142688194 -0.008390450 -0.079409235
## PC1 PC2 PC3 PC4 htn_meds lipid_meds
## -0.255295437 0.892419943 -0.328648024 0.068159506 0.081275342 -0.106706163
## nitro_meds t2d_meds
## -0.013938767 0.158049394time_pval_adj <- p.adjust(out_dat$time_pval, method="BH")
out_dat1 <- add_column(out_dat, time_pval_adj, .after = 2)
out_dat2 <- out_dat1[order(out_dat1$time_pval), ]
out_dat3 <- left_join(metab.look.up.table, out_dat2, by=c("fake.metab.names" = "metabolite")) %>%
mutate(metabolite = orig.metab.names)
out_dat3 <- out_dat3[order(out_dat3$time_pval), ]
write.csv(out_dat3, "Age_rel.change.assoc_batch5.pc_gee.06.22.2024.csv", row.names=F)#Annotation
annot.dat <- read.csv(paste0(annot.dir, "llfs.annotation.03.30.2023.csv")) %>%
mutate(Compound.Name = Input.name)
sum(out_dat3$metabolite %in% annot.dat$Compound.Name)## [1] 220#188
out_dat1_annot <- data.frame(annot.dat, out_dat3[match(annot.dat$Compound.Name, out_dat3$metabolite),])
out_dat1_annot <- out_dat1_annot[order(out_dat1_annot$Age_pval), ]
write.csv(out_dat1_annot, "annotated_Age_rel.change_assoc_batch5.pc_gee.06.22.2024.csv")Plot
metab <- as.character(out_dat3$fake.metab.names[1:100])
true.name <- as.character(out_dat3$metabolite[1:100])
for(i in 1:30){
plot.data <- analysis.final.no.missing.dat %>%
select( c("subject", metab=metab[i], "Age.e", "delta.t","visitcode")) %>%
mutate( Age.p = Age.e+delta.t)
png(paste0("plot_dir/",true.name[i], "_lines.png"))
print(ggplot(plot.data, aes(x=Age.p, y=metab, group=subject)) +
geom_line() +
theme_bw() +
ylab(paste0("Age", true.name[i])))
dev.off()
}#Plot for paper
analysis.final.no.missing.dat <- read.csv("analysis.final.no.missing.dat.batch5.csv", header=T)
plot(analysis.final.no.missing.dat$Age.e, analysis.final.no.missing.dat$metab53)
# llfs.data.batch5.phen <- data.frame(llfs.data.batch5,
# analysis.final.no.missing.dat$metab53[match(llfs.data.batch5$subject,
# analysis.final.no.missing.dat$subject)])
#write.csv(llfs.data.batch5.phen,"bad_phenylalanin.csv", row.names=F)
kynurenine <- analysis.final.no.missing.dat$metab81
tryptophan <- analysis.final.no.missing.dat$metab26
tryptophan.betaine <- analysis.final.no.missing.dat$metab67
N.ACETYLTRYPTOPHAN <- analysis.final.no.missing.dat$metab11
Ergothioneine <- analysis.final.no.missing.dat$metab40
Tartaric.Acid <- analysis.final.no.missing.dat$metab105
Melatonin <- analysis.final.no.missing.dat$metab62
Gamma.Linolenic.Acid <- analysis.final.no.missing.dat$metab138
Cortisol <- analysis.final.no.missing.dat$metab6
Creatine <- analysis.final.no.missing.dat$metab9
Warfarin <- analysis.final.no.missing.dat$metab86
Seven.Hydroxy.3.Methylflavone <- analysis.final.no.missing.dat$metab218
Acesulfame <- analysis.final.no.missing.dat$metab211
Glucose <- analysis.final.no.missing.dat$metab207
Norvaline <- analysis.final.no.missing.dat$metab5
Glycocholic <- analysis.final.no.missing.dat$metab220
N.ACETYLSEROTONIN <- analysis.final.no.missing.dat$metab12
urate <- analysis.final.no.missing.dat$metab155
plot.data <- analysis.final.no.missing.dat %>%
select( c("subject", "Age.e", "delta.t","visitcode")) %>%
mutate( Age.p = Age.e+delta.t)
data.plot <- data.frame(plot.data,
kynurenine, tryptophan, tryptophan.betaine, N.ACETYLTRYPTOPHAN, Ergothioneine, Tartaric.Acid, Melatonin,
Gamma.Linolenic.Acid, Cortisol, Creatine, Warfarin, Seven.Hydroxy.3.Methylflavone, Acesulfame, Glucose,
Norvaline, Glycocholic,N.ACETYLSEROTONIN, urate )
for(i in 6:23){
p<- ggplot2::ggplot(data=data.plot, aes(x=Age.p, y=data.plot%>% pull(i), group=subject))+
geom_line(size=1) +
theme_bw() +
xlab("Age")+ylab(names(data.plot)[i])+
theme(axis.text.x = element_text(face="bold", color="#993333",
size=25, angle=0, vjust = 0.25, hjust=+0.5),
axis.text.y = element_text(face="bold", color="#993333",
size=25, angle=0, vjust = 0.25, hjust=-1.0),
text = element_text(family = "Arial", size=25))
print(p)
}## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## i Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
